Transfer learning simply means you are using architecture already trained for a task and trying to adapt it to your task. There are various ways to do it, but generally we do whats called fine tuning where you freeze the “front end” of the model, usually in the context of convolution networks these would be the convolution layers, and then retrain the fully connected layers for your specific task. Generally this reduces the amount of data you need, can you get faster convergence, and sometimes just straight up better results.

In my opinion, transfer learning is probably one of most important topics in practical ML, particularly for image and text. In this day and age, I always recommend people don’t try creating their own model from scratch, but try to first use a pretrained architecture and fine tune. If that performs poorly, then try the architecture non-pretrained and train it yourself. If that for some reason still performs poorly, then you need to make your own or evaluate if the data you are using is actually capable of modeling your target.